This chapter introduces regression as the machine learning framework for predicting continuous numeric outcomes such as prices, demand, and revenue. It explains the progression from linear and regularized regression to tree-based models and industry workhorses like Random Forest and XGBoost.

This chapter introduces the major machine learning problem families based on the type of output a model produces: numbers, categories, groups, ranked lists, or future sequences. It provides a structured roadmap of regression, classification, clustering, recommendation, and forecasting models that form the foundation of real-world AI systems.

This chapter introduces what a machine learning model truly is: a learned mathematical function that maps inputs to outputs. It explains how models differ from algorithms, how supervised and unsupervised learning work, and why models matter inside real business decision systems.

From model training to real-world deployment, this chapter explains the critical steps that turn machine learning into a business-ready system. Learn how organizations finalize models, deploy with A/B testing, monitor drift, and continuously improve AI in production.

This chapter explains how machine learning models are trained, tested, improved, and selected, while avoiding overfitting, underfitting, and model drift, to ensure they deliver real business value.

In most practical applications, data scientists often have a set of ML models that can be applied to solve a problem. Data scientists run a set of ML models and see which ones perform the best. This is called Racing ML models against each other to choose a winner.